Cyber-Attack Prediction via Sequence Signature Analysis
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Solution Overview
Problem
Current cyber-security solutions struggle to predict subsequent attacks in attack campaigns due to the high volume of security events, which leads to false positives, misdetected attacks, and inability to distinguish between critical and uncritical events, resulting in delayed or inaccurate detection of cyber-attacks.
Innovation Solution
A method and system that process events data to extract sequences of attack vectors, generate sequence signatures, and compare them to historic signatures to predict subsequent cyber-attacks, utilizing a processing circuitry and memory to analyze and determine potential future attacks within a network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security devices generate and collect all security events to ensure comprehensive attack detection, then the coverage of security monitoring is improved, but the volume of events to be analyzed increases significantly, making manual processing ineffective
Solution Approach 1:
The patent extracts and isolates only the most critical security events from the massive volume of generated events by using automated prioritization algorithms and machine learning models. This extraction process separates critical events requiring immediate attention from routine events, enabling focused analysis without being overwhelmed by the total event volume while maintaining comprehensive detection coverage.
Solution Approach 2:
The patent introduces automated analysis systems and intelligent algorithms as intermediaries between security event generation and human administrator analysis. These intermediary systems automatically process, filter, prioritize, and correlate events before presenting them to administrators, effectively managing the transition from high-volume raw events to manageable critical event sets.
2Measurement precision
If administrators manually analyze security events to improve detection accuracy, then the precision of attack detection is improved, but the time required to process events increases and scalability is reduced
Solution Approach 1:
The patent performs preliminary automated analysis, filtering, and prioritization of security events before they reach human administrators. Machine learning models pre-process events to identify patterns, correlate related events, and predict potential attacks in advance, so that when administrators do review events, the most time-critical and suspicious events are already prepared and contextualized, significantly reducing their analysis time while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis of security events with automated intelligent systems using machine learning and artificial intelligence. These systems automatically detect patterns, correlate events across multiple sources, and predict attacks without human intervention for routine analysis, freeing administrators to focus only on complex cases requiring human judgment while dramatically reducing overall analysis time.
3Reliability
If all security events are analyzed to reduce false positives, then the reliability of attack detection is improved, but the resource consumption and processing overhead increase
Solution Approach 1:
The patent applies different levels of analysis and processing intensity to different types of events based on their characteristics and risk levels. Critical events receive comprehensive automated analysis with multiple verification layers, while routine events receive streamlined processing. This localized quality approach ensures high detection reliability for important events while maintaining processing efficiency across the entire event stream by not applying maximum resources uniformly to all events.
Solution Approach 2:
The patent dynamically adjusts analysis parameters such as detection thresholds, correlation windows, and processing depth based on the current security context, event types, and organizational risk profiles. Machine learning models continuously optimize these parameters to balance detection reliability with processing efficiency, automatically increasing scrutiny for suspicious patterns while reducing overhead for benign events, thereby maintaining high reliability without constant maximum resource consumption.
Data Source
AI summary
A method and system for predicting subsequent cyber-attacks in attack campaigns are provided. The method includes receiving events data related to cyber-attacks occurring in a network during a predefined time window; extracting at least one sequence from the received events data at least one attack vector; generating a sequence signature for each of the at least one extracted sequence; comparing each sequence signature to a representation of historic sequence signatures to determine at least partially matching sequence signature; and based on the matching sequence, determining at least one subsequent cyber-attack in a respective sequence.